Search Results for author:
Found 6 papers, 0 papers with code
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner.
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipping points in the emergent behavior of complex systems, and (b) characterizing probabilities of rare events (here, catastrophic shifts) near them.
Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors
Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures.
Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information
Iterates of such neural-network models allow for learning from data sampled at arbitrary time points $\textit{without}$ data modification.
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output.
Initializing LSTM internal states via manifold learning
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of LSTM recurrent neural networks, ensuring consistency with the initial observed input data.
